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Knowledge-driven 3-D extraction of the masseter from MR data.

H P Ng¹, S H Ong, K W C Foong

¹NUS Graduate School for Integrative Sciences & Engineering, Singapore.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|October 20, 2007

Summary

This study presents an automated method to extract the masseter muscle from MRI scans. The novel approach achieves 83.5% accuracy, aiding clinical analysis of mastication muscles.

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Area of Science:

Medical Imaging
Anatomy
Biomedical Engineering

Background:

The masseter muscle, crucial for mastication, requires accurate segmentation in medical imaging for clinical assessment.
Previous methods involved manual or less automated 2-D segmentation of the masseter muscle from magnetic resonance (MR) images.

Purpose of the Study:

To develop a knowledge-driven, highly automatic methodology for 3-D masseter muscle extraction from MR data sets.
To improve the efficiency and accuracy of masseter segmentation for clinical applications.

Main Methods:

A novel methodology utilizing ground truth to identify the optimal MR slice for initial 2-D masseter segmentation.
Employing the 2-D segmentation to define the region of interest (ROI) in adjacent slices.
Determining masseter extraction thresholds via histogram analysis of the segmented 2-D masseter.

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Utilizing volume rendering for 3-D visualization of the segmented masseter muscle.

Main Results:

The proposed methodology was successfully applied to five MR data sets.
Validation against manual contour tracings yielded an average accuracy of 83.5% for masseter segmentation.
The automated approach demonstrated high precision in extracting the masseter muscle.

Conclusions:

The developed knowledge-driven methodology offers a highly automatic and accurate solution for 3-D masseter muscle segmentation from MR data.
This technique has significant potential for enhancing clinical evaluation and research related to the mastication system.
The high accuracy achieved validates the effectiveness of the proposed automated segmentation approach.